HarvardX: Data Science: Inference and Modeling

4.3 stars
20 ratings

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

8 weeks
1–2 hours per week
Self-paced
Free

125,636 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 28
Ends Dec 20
Starts Nov 28
Starts Apr 17, 2024

Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.

This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.

Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.

At a glance

• Language: English
• Video Transcript: English
• Associated programs:
• Associated skills: Probability, Bayesian Modeling, Confidence Intervals, Forecasting, Data Analysis, Data Science

What you'll learn

Skip What you'll learn
• The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
• How to use models to aggregatedata from different sources
• The very basics of Bayesian statistics and predictive modeling

Honor code statement
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

Research statement
By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX's instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University's stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.